降低模糊邻域聚类算法空间复杂度的研究

2017 International Conference on Computer Science and Engineering (UBMK) Pub Date : 2017-10-01 DOI:10.1109/UBMK.2017.8093467

C. Atilgan, E. Nasibov

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引用次数: 0

摘要

在基于密度的聚类中使用模糊邻域关系，如模糊结合点(FJP)算法，可以产生更加鲁棒和自治的算法。尽管基于模糊邻域的聚类方法被证明速度足够快，可以在1秒内处理数万个数据，但空间复杂性仍然是一个限制因素。本文提出了一种低空间复杂度的变换FJP算法。

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On reducing space complexity of fuzzy neighborhood based clustering algorithms

Using fuzzy neighborhood relations in density-based clustering, like in Fuzzy Joint Points (FJP) algorithm, yields more robust and autonomous algorithms. Even though the fuzzy neighborhood based clustering methods are proven to be fast enough, such that tens of thousands of data can be handled under a second, the space complexity is still a limiting factor. In this study, a transformed FJP algorithm with low space complexity is proposed.

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2017 International Conference on Computer Science and Engineering (UBMK)

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